Scientific Data Processing

Advanced8 Steps7h 30m46 Credits

Big data, machine learning, and scientific data? It sounds like the perfect match. In this advanced-level quest, you will get hands-on practice with GCP services like Big Query, Dataproc, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.

InfrastructureDataBusiness TransformationMachine Learning

Prerequisites

This Quest requires hands-on experience with GCP data processing and machine learning services like Dataproc, Dataflow, and Cloud ML Engine. It is recommended that the student have at least earned a Badge by completing the hands-on labs in the Baseline: Data, ML, and AI Quest before beginning.

In this lab you spin up a virtual machine, configure its security, access it remotely, and then carry out the steps of an ingest-transform-and-publish data pipeline manually. This lab is part of a series of labs on processing scientific data.

In this lab you train, evaluate, and deploy a machine learning model to predict a baby’s weight. You then send requests to the model to make online predictions. This lab is part of a series of labs on processing scientific data.

In this lab, you carry out a transfer learning example based on Inception-v3 image recognition neural network. The objective is to classify coastline images captured using drones based on their potential for flood damage.